most citedConcurrence: A dependence criterion for time series, applied to biological data

1 citations · 1 across the 2 of their papers we have counts for

collaborators

6 papers

cs.CV2026

Micro-DualNet: Dual-Path Spatio-Temporal Network for Micro-Action Recognition

Naga VS Raviteja Chappa, Evangelos Sariyanidi, Lisa Yankowitz +4

Micro-actions are subtle, localized movements lasting 1-3 seconds such as scratching one's head or tapping fingers. Such subtle actions are essential for social communication, ubiq…

eess.SP20261 cited

Concurrence: A dependence criterion for time series, applied to biological data

Evangelos Sariyanidi, John D. Herrington, Lisa Yankowitz +8

Measuring the statistical dependence between observed signals is a primary tool for scientific discovery. However, biological systems often exhibit complex non-linear interactions…

cs.CV2025

Bitbox: Behavioral Imaging Toolbox for Computational Analysis of Behavior from Videos

Evangelos Sariyanidi, Gokul Nair, Lisa Yankowitz +8

Computational measurement of human behavior from video has recently become feasible due to major advances in AI. These advances now enable granular and precise quantification of fa…

cs.HC2025

Developer Insights into Designing AI-Based Computer Perception Tools

Maya Guhan, Meghan E. Hurley, Eric A. Storch +5

Artificial intelligence (AI)-based computer perception (CP) technologies use mobile sensors to collect behavioral and physiological data for clinical decision-making. These tools c…

eess.SP2025

Measuring Dependencies between Biological Signals with Self-supervision, and its Limitations

Evangelos Sariyanidi, John D. Herrington, Lisa Yankowitz +6

Measuring the statistical dependence between observed signals is a primary tool for scientific discovery. However, biological systems often exhibit complex non-linear interactions…

cs.HC2025

Stakeholder Perspectives on Humanistic Implementation of Computer Perception in Healthcare: A Qualitative Study

Kristin M. Kostick-Quenet, Meghan E. Hurley, Syed Ayaz +7

Computer perception (CP) technologies (digital phenotyping, affective computing and related passive sensing approaches) offer unprecedented opportunities to personalize healthcare,…